{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Comparison of predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- See preprocessing of gene expression and prediction were done in CaDRReS2/pipeline/* and 03_*\n",
    "- Convert predicted delta to cv to cell death percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:46.163989Z",
     "start_time": "2020-08-24T05:16:45.464431Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import metrics\n",
    "from collections import Counter\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('precision', 2)\n",
    "np.set_printoptions(suppress=True)\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:46.168828Z",
     "start_time": "2020-08-24T05:16:46.165647Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compare between cell line prediction and cell type-specific prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:46.173967Z",
     "start_time": "2020-08-24T05:16:46.171436Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_shifted = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for experimental validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:58.444220Z",
     "start_time": "2020-08-24T05:16:58.440900Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_used = '3 fold' # All for HN, '9 fold' '3 fold' 'Median IC50' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for calculating % cell death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:58.739193Z",
     "start_time": "2020-08-24T05:16:58.736806Z"
    }
   },
   "outputs": [],
   "source": [
    "# log2_median_ic50, log2_median_ic50_9f, log2_median_ic50_hn, log2_median_ic50_9f_hn, log2_median_ic50_3f_hn, log2_max_conc\n",
    "dosage_ref = 'log2_median_ic50_hn' # log2_median_ic50_hn | log2_median_ic50_3f_hn\n",
    "model_name = 'hn_drug_cw_dw10_100000_model'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:59.251334Z",
     "start_time": "2020-08-24T05:16:59.248957Z"
    }
   },
   "outputs": [],
   "source": [
    "current_dir = '../result/HN_model/patient_TPM/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:59.390401Z",
     "start_time": "2020-08-24T05:16:59.380586Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "else:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}.csv'.format(dosage_ref, model_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:59.811110Z",
     "start_time": "2020-08-24T05:16:59.649494Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File name</th>\n",
       "      <th>Dosage</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>1</td>\n",
       "      <td>56.98</td>\n",
       "      <td>64.74</td>\n",
       "      <td>40.52</td>\n",
       "      <td>26.77</td>\n",
       "      <td>97.41</td>\n",
       "      <td>67.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>2</td>\n",
       "      <td>58.10</td>\n",
       "      <td>64.20</td>\n",
       "      <td>38.30</td>\n",
       "      <td>25.42</td>\n",
       "      <td>96.90</td>\n",
       "      <td>68.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>3</td>\n",
       "      <td>53.09</td>\n",
       "      <td>59.52</td>\n",
       "      <td>35.23</td>\n",
       "      <td>24.12</td>\n",
       "      <td>93.86</td>\n",
       "      <td>66.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1</td>\n",
       "      <td>81.52</td>\n",
       "      <td>71.17</td>\n",
       "      <td>84.23</td>\n",
       "      <td>86.19</td>\n",
       "      <td>88.00</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>2</td>\n",
       "      <td>80.69</td>\n",
       "      <td>67.84</td>\n",
       "      <td>78.09</td>\n",
       "      <td>81.28</td>\n",
       "      <td>86.22</td>\n",
       "      <td>70.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               File name  Dosage       Drug  Replicate  HN120  \\\n",
       "0  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          1  56.98   \n",
       "1  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          2  58.10   \n",
       "2  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          3  53.09   \n",
       "6  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          1  81.52   \n",
       "7  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          2  80.69   \n",
       "\n",
       "   HN137  HN148  HN159  HN160  HN182  \n",
       "0  64.74  40.52  26.77  97.41  67.09  \n",
       "1  64.20  38.30  25.42  96.90  68.63  \n",
       "2  59.52  35.23  24.12  93.86  66.52  \n",
       "6  71.17  84.23  86.19  88.00  67.97  \n",
       "7  67.84  78.09  81.28  86.22  70.59  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Combined replicate #####\n",
    "\n",
    "validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "cv_single_df_list = []\n",
    "cv_combi_df_list = []\n",
    "\n",
    "for validation_fname in validation_fname_list:\n",
    "\n",
    "    cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "    cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_single_df = cv_single_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index() # just to rearrange columns\n",
    "\n",
    "    cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "    cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_combi_df = cv_combi_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index()\n",
    "    \n",
    "    cv_single_df_list += [cv_single_df]\n",
    "    cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:59.815811Z",
     "start_time": "2020-08-24T05:16:59.813020Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "##### Not combined replicate #####\n",
    "\n",
    "# validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "# cv_single_df_list = []\n",
    "# cv_combi_df_list = []\n",
    "\n",
    "# for validation_fname in validation_fname_list:\n",
    "\n",
    "#     cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "#     cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "#     cv_single_df = cv_single_df.reset_index()\n",
    "\n",
    "#     cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "#     cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "#     cv_combi_df = cv_combi_df.reset_index()\n",
    "    \n",
    "#     cv_single_df_list += [cv_single_df]\n",
    "#     cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "# cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "# cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:16:59.915281Z",
     "start_time": "2020-08-24T05:16:59.911751Z"
    }
   },
   "outputs": [],
   "source": [
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160', 'HN182']\n",
    "patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN160']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN160']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "single_drug_id_list = [1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "single_drug_list = ['Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "combi_drug_list = ['Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Gefitinib|Epothilone B'] + ['Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103']\n",
    "\n",
    "# single_drug_id_list = [1007, 133, 1010, 182, 301, 302]\n",
    "# single_drug_list = ['Docetaxel', 'Doxorubicin', 'Gefitinib', 'Obatoclax Mesylate', 'PHA-793887', 'PI-103']\n",
    "# combi_drug_list = ['Docetaxel|Gefitinib']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:00.043845Z",
     "start_time": "2020-08-24T05:17:00.035164Z"
    }
   },
   "outputs": [],
   "source": [
    "# read reference dosages file\n",
    "dosage_df = pd.read_csv('../preprocessed_data/GDSC/hn_drug_stat.csv', index_col=0)\n",
    "dosage_df = dosage_df.loc[single_drug_id_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:00.163441Z",
     "start_time": "2020-08-24T05:17:00.161133Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "##### Check consistency across triplicate #####\n",
    "\n",
    "# validation_fname = validation_fname_list[0]\n",
    "\n",
    "# cv_new_df = pd.concat([pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2]), pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])], axis=0).reset_index()\n",
    "# cv_new_df = cv_new_df[~cv_new_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_new_df = cv_new_df.set_index(['Drug', 'Dosage', 'Replicate']).stack().reset_index()\n",
    "# cv_new_df.columns = ['Drug', 'Dosage', 'Replicate', 'Patient', 'Viability']\n",
    "# cv_new_df = cv_new_df.groupby(['Dosage', 'Drug', 'Patient', 'Replicate']).sum().unstack()\n",
    "\n",
    "# sns.pairplot(cv_new_df, plot_kws={'s':10, 'alpha':0.75, 'linewidth':0}, diag_kws={'bins':20}, aspect=1.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Preprocess predicted and observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:00.423477Z",
     "start_time": "2020-08-24T05:17:00.414839Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_kill_df = 100 - cv_df.set_index(['Dosage', 'Drug', 'File name', 'Replicate']).astype(float)\n",
    "obs_kill_df = obs_kill_df.loc[dosage_used]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:00.552766Z",
     "start_time": "2020-08-24T05:17:00.540711Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Afatinib</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>43.02</td>\n",
       "      <td>35.26</td>\n",
       "      <td>59.48</td>\n",
       "      <td>73.23</td>\n",
       "      <td>2.59</td>\n",
       "      <td>32.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.90</td>\n",
       "      <td>35.80</td>\n",
       "      <td>61.70</td>\n",
       "      <td>74.58</td>\n",
       "      <td>3.10</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.91</td>\n",
       "      <td>40.48</td>\n",
       "      <td>64.77</td>\n",
       "      <td>75.88</td>\n",
       "      <td>6.14</td>\n",
       "      <td>33.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Docetaxel</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>18.48</td>\n",
       "      <td>28.83</td>\n",
       "      <td>15.77</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.00</td>\n",
       "      <td>32.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.31</td>\n",
       "      <td>32.16</td>\n",
       "      <td>21.91</td>\n",
       "      <td>18.72</td>\n",
       "      <td>13.78</td>\n",
       "      <td>29.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.69</td>\n",
       "      <td>38.61</td>\n",
       "      <td>24.50</td>\n",
       "      <td>24.83</td>\n",
       "      <td>14.27</td>\n",
       "      <td>28.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Doxorubicin</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>33.32</td>\n",
       "      <td>42.17</td>\n",
       "      <td>23.79</td>\n",
       "      <td>15.58</td>\n",
       "      <td>14.64</td>\n",
       "      <td>45.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.72</td>\n",
       "      <td>46.45</td>\n",
       "      <td>25.15</td>\n",
       "      <td>22.23</td>\n",
       "      <td>13.93</td>\n",
       "      <td>41.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39.04</td>\n",
       "      <td>52.38</td>\n",
       "      <td>31.03</td>\n",
       "      <td>25.80</td>\n",
       "      <td>17.89</td>\n",
       "      <td>45.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B</th>\n",
       "      <th>validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>50.24</td>\n",
       "      <td>60.62</td>\n",
       "      <td>30.33</td>\n",
       "      <td>31.60</td>\n",
       "      <td>28.97</td>\n",
       "      <td>55.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              HN120  HN137  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          43.02  35.26   \n",
       "                                                   2          41.90  35.80   \n",
       "                                                   3          46.91  40.48   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          18.48  28.83   \n",
       "                                                   2          19.31  32.16   \n",
       "                                                   3          23.69  38.61   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          33.32  42.17   \n",
       "                                                   2          35.72  46.45   \n",
       "                                                   3          39.04  52.38   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          50.24  60.62   \n",
       "\n",
       "                                                              HN148  HN159  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          59.48  73.23   \n",
       "                                                   2          61.70  74.58   \n",
       "                                                   3          64.77  75.88   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          15.77  13.81   \n",
       "                                                   2          21.91  18.72   \n",
       "                                                   3          24.50  24.83   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          23.79  15.58   \n",
       "                                                   2          25.15  22.23   \n",
       "                                                   3          31.03  25.80   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          30.33  31.60   \n",
       "\n",
       "                                                              HN160  HN182  \n",
       "Drug         File name                             Replicate                \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1           2.59  32.91  \n",
       "                                                   2           3.10  31.37  \n",
       "                                                   3           6.14  33.48  \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          12.00  32.03  \n",
       "                                                   2          13.78  29.41  \n",
       "                                                   3          14.27  28.43  \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          14.64  45.80  \n",
       "                                                   2          13.93  41.51  \n",
       "                                                   3          17.89  45.25  \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          28.97  55.86  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_kill_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:00.692036Z",
     "start_time": "2020-08-24T05:17:00.673449Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_df = pred_single_df[pred_single_df['drug_id'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['drug_id_A'].isin(single_drug_id_list) & pred_combi_df['drug_id_B'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df.loc[:, 'Combi Name 1'] = pred_combi_df['drug_name_A'].values + '|' + pred_combi_df['drug_name_B'].values\n",
    "pred_combi_df.loc[:, 'Combi Name 2'] = pred_combi_df['drug_name_B'].values + '|' + pred_combi_df['drug_name_A'].values\n",
    "\n",
    "temp1 = pred_combi_df['Combi Name 1'][pred_combi_df['Combi Name 1'].isin(combi_drug_list)]\n",
    "temp2 = pred_combi_df['Combi Name 2'][pred_combi_df['Combi Name 2'].isin(combi_drug_list)]\n",
    "combi_name = pd.concat([temp1, temp2]).values\n",
    "\n",
    "pred_combi_df = pd.concat([pred_combi_df[pred_combi_df['Combi Name 1'].isin(combi_drug_list)], pred_combi_df[pred_combi_df['Combi Name 2'].isin(combi_drug_list)]])\n",
    "pred_combi_df.loc[:, 'Combi Name'] = combi_name\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['Combi Name'].isin(combi_drug_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:00.805864Z",
     "start_time": "2020-08-24T05:17:00.798604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id</th>\n",
       "      <th>kill</th>\n",
       "      <th>drug_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>10.28</td>\n",
       "      <td>Docetaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1010</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1012</td>\n",
       "      <td>60.44</td>\n",
       "      <td>Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>86.28</td>\n",
       "      <td>Doxorubicin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>HN120</td>\n",
       "      <td>182</td>\n",
       "      <td>75.53</td>\n",
       "      <td>Obatoclax Mesylate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient  drug_id   kill           drug_name\n",
       "4    HN120     1007  10.28           Docetaxel\n",
       "5    HN120     1010  16.60           Gefitinib\n",
       "6    HN120     1012  60.44          Vorinostat\n",
       "24   HN120      133  86.28         Doxorubicin\n",
       "44   HN120      182  75.53  Obatoclax Mesylate"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_single_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:00.953022Z",
     "start_time": "2020-08-24T05:17:00.918645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id_A</th>\n",
       "      <th>drug_name_A</th>\n",
       "      <th>drug_id_B</th>\n",
       "      <th>drug_name_B</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_kill_A</th>\n",
       "      <th>cluster_kill_B</th>\n",
       "      <th>kill_A</th>\n",
       "      <th>kill_B</th>\n",
       "      <th>...</th>\n",
       "      <th>kill_C.1</th>\n",
       "      <th>improve</th>\n",
       "      <th>improve.1</th>\n",
       "      <th>improve_p</th>\n",
       "      <th>improve_p.1</th>\n",
       "      <th>sum_kill_dif</th>\n",
       "      <th>sum_kill_dif.1</th>\n",
       "      <th>Combi Name 1</th>\n",
       "      <th>Combi Name 2</th>\n",
       "      <th>Combi Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>HN148</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>50.31</td>\n",
       "      <td>45.66</td>\n",
       "      <td>50.31</td>\n",
       "      <td>45.66</td>\n",
       "      <td>...</td>\n",
       "      <td>73.00</td>\n",
       "      <td>22.69</td>\n",
       "      <td>22.69</td>\n",
       "      <td>4.51e-01</td>\n",
       "      <td>4.51e-01</td>\n",
       "      <td>4.65</td>\n",
       "      <td>4.65</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>HN137</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>70.44</td>\n",
       "      <td>74.00</td>\n",
       "      <td>70.44</td>\n",
       "      <td>74.00</td>\n",
       "      <td>...</td>\n",
       "      <td>92.31</td>\n",
       "      <td>18.32</td>\n",
       "      <td>18.32</td>\n",
       "      <td>2.48e-01</td>\n",
       "      <td>2.48e-01</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.56</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>HN148</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>24.02</td>\n",
       "      <td>28.19</td>\n",
       "      <td>24.02</td>\n",
       "      <td>28.19</td>\n",
       "      <td>...</td>\n",
       "      <td>45.44</td>\n",
       "      <td>17.25</td>\n",
       "      <td>17.25</td>\n",
       "      <td>6.12e-01</td>\n",
       "      <td>6.12e-01</td>\n",
       "      <td>4.16</td>\n",
       "      <td>4.16</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>22.47</td>\n",
       "      <td>21.40</td>\n",
       "      <td>22.47</td>\n",
       "      <td>21.40</td>\n",
       "      <td>...</td>\n",
       "      <td>39.07</td>\n",
       "      <td>16.59</td>\n",
       "      <td>16.59</td>\n",
       "      <td>7.38e-01</td>\n",
       "      <td>7.38e-01</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>HN160</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>25.28</td>\n",
       "      <td>34.91</td>\n",
       "      <td>25.28</td>\n",
       "      <td>34.91</td>\n",
       "      <td>...</td>\n",
       "      <td>51.37</td>\n",
       "      <td>16.46</td>\n",
       "      <td>16.46</td>\n",
       "      <td>4.71e-01</td>\n",
       "      <td>4.71e-01</td>\n",
       "      <td>9.63</td>\n",
       "      <td>9.63</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>HN137</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>74.37</td>\n",
       "      <td>61.77</td>\n",
       "      <td>74.37</td>\n",
       "      <td>61.77</td>\n",
       "      <td>...</td>\n",
       "      <td>90.20</td>\n",
       "      <td>15.83</td>\n",
       "      <td>15.83</td>\n",
       "      <td>2.13e-01</td>\n",
       "      <td>2.13e-01</td>\n",
       "      <td>12.60</td>\n",
       "      <td>12.60</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>77.30</td>\n",
       "      <td>84.90</td>\n",
       "      <td>77.30</td>\n",
       "      <td>84.90</td>\n",
       "      <td>...</td>\n",
       "      <td>96.57</td>\n",
       "      <td>11.67</td>\n",
       "      <td>11.67</td>\n",
       "      <td>1.37e-01</td>\n",
       "      <td>1.37e-01</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.60</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>HN148</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>24.02</td>\n",
       "      <td>59.74</td>\n",
       "      <td>24.02</td>\n",
       "      <td>59.74</td>\n",
       "      <td>...</td>\n",
       "      <td>69.41</td>\n",
       "      <td>9.67</td>\n",
       "      <td>9.67</td>\n",
       "      <td>1.62e-01</td>\n",
       "      <td>1.62e-01</td>\n",
       "      <td>35.72</td>\n",
       "      <td>35.72</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>HN159</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>69.26</td>\n",
       "      <td>29.00</td>\n",
       "      <td>69.26</td>\n",
       "      <td>29.00</td>\n",
       "      <td>...</td>\n",
       "      <td>78.17</td>\n",
       "      <td>8.91</td>\n",
       "      <td>8.91</td>\n",
       "      <td>1.29e-01</td>\n",
       "      <td>1.29e-01</td>\n",
       "      <td>40.26</td>\n",
       "      <td>40.26</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>10.28</td>\n",
       "      <td>16.60</td>\n",
       "      <td>10.28</td>\n",
       "      <td>16.60</td>\n",
       "      <td>...</td>\n",
       "      <td>25.17</td>\n",
       "      <td>8.57</td>\n",
       "      <td>8.57</td>\n",
       "      <td>5.16e-01</td>\n",
       "      <td>5.16e-01</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.33</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>HN159</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>9.64</td>\n",
       "      <td>11.32</td>\n",
       "      <td>9.64</td>\n",
       "      <td>11.32</td>\n",
       "      <td>...</td>\n",
       "      <td>19.87</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.55</td>\n",
       "      <td>7.55e-01</td>\n",
       "      <td>7.55e-01</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.68</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>86.28</td>\n",
       "      <td>60.44</td>\n",
       "      <td>86.28</td>\n",
       "      <td>60.44</td>\n",
       "      <td>...</td>\n",
       "      <td>94.57</td>\n",
       "      <td>8.29</td>\n",
       "      <td>8.29</td>\n",
       "      <td>9.61e-02</td>\n",
       "      <td>9.61e-02</td>\n",
       "      <td>25.84</td>\n",
       "      <td>25.84</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>HN160</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>9.29</td>\n",
       "      <td>15.82</td>\n",
       "      <td>9.29</td>\n",
       "      <td>15.82</td>\n",
       "      <td>...</td>\n",
       "      <td>23.64</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.82</td>\n",
       "      <td>4.94e-01</td>\n",
       "      <td>4.94e-01</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.53</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>HN160</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>9.29</td>\n",
       "      <td>7.50</td>\n",
       "      <td>9.29</td>\n",
       "      <td>7.50</td>\n",
       "      <td>...</td>\n",
       "      <td>16.10</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "      <td>7.33e-01</td>\n",
       "      <td>7.33e-01</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>22.47</td>\n",
       "      <td>70.44</td>\n",
       "      <td>22.47</td>\n",
       "      <td>70.44</td>\n",
       "      <td>...</td>\n",
       "      <td>77.08</td>\n",
       "      <td>6.64</td>\n",
       "      <td>6.64</td>\n",
       "      <td>9.43e-02</td>\n",
       "      <td>9.43e-02</td>\n",
       "      <td>47.96</td>\n",
       "      <td>47.96</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>HN148</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>8.65</td>\n",
       "      <td>24.02</td>\n",
       "      <td>8.65</td>\n",
       "      <td>24.02</td>\n",
       "      <td>...</td>\n",
       "      <td>30.59</td>\n",
       "      <td>6.57</td>\n",
       "      <td>6.57</td>\n",
       "      <td>2.74e-01</td>\n",
       "      <td>2.74e-01</td>\n",
       "      <td>15.37</td>\n",
       "      <td>15.37</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>HN137</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>70.44</td>\n",
       "      <td>21.40</td>\n",
       "      <td>70.44</td>\n",
       "      <td>21.40</td>\n",
       "      <td>...</td>\n",
       "      <td>76.76</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.33</td>\n",
       "      <td>8.98e-02</td>\n",
       "      <td>8.98e-02</td>\n",
       "      <td>49.03</td>\n",
       "      <td>49.03</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>HN160</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>7.50</td>\n",
       "      <td>15.82</td>\n",
       "      <td>7.50</td>\n",
       "      <td>15.82</td>\n",
       "      <td>...</td>\n",
       "      <td>22.14</td>\n",
       "      <td>6.32</td>\n",
       "      <td>6.32</td>\n",
       "      <td>3.99e-01</td>\n",
       "      <td>3.99e-01</td>\n",
       "      <td>8.32</td>\n",
       "      <td>8.32</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>HN159</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>44.88</td>\n",
       "      <td>11.32</td>\n",
       "      <td>44.88</td>\n",
       "      <td>11.32</td>\n",
       "      <td>...</td>\n",
       "      <td>51.12</td>\n",
       "      <td>6.24</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1.39e-01</td>\n",
       "      <td>1.39e-01</td>\n",
       "      <td>33.56</td>\n",
       "      <td>33.56</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>HN148</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>8.65</td>\n",
       "      <td>28.19</td>\n",
       "      <td>8.65</td>\n",
       "      <td>28.19</td>\n",
       "      <td>...</td>\n",
       "      <td>34.40</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.21</td>\n",
       "      <td>2.20e-01</td>\n",
       "      <td>2.20e-01</td>\n",
       "      <td>19.54</td>\n",
       "      <td>19.54</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>HN182</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>8.72</td>\n",
       "      <td>28.95</td>\n",
       "      <td>8.72</td>\n",
       "      <td>28.95</td>\n",
       "      <td>...</td>\n",
       "      <td>35.14</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.20</td>\n",
       "      <td>2.14e-01</td>\n",
       "      <td>2.14e-01</td>\n",
       "      <td>20.22</td>\n",
       "      <td>20.22</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>HN159</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>44.88</td>\n",
       "      <td>86.86</td>\n",
       "      <td>44.88</td>\n",
       "      <td>86.86</td>\n",
       "      <td>...</td>\n",
       "      <td>92.76</td>\n",
       "      <td>5.90</td>\n",
       "      <td>5.90</td>\n",
       "      <td>6.79e-02</td>\n",
       "      <td>6.79e-02</td>\n",
       "      <td>41.98</td>\n",
       "      <td>41.98</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>HN159</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>9.64</td>\n",
       "      <td>44.88</td>\n",
       "      <td>9.64</td>\n",
       "      <td>44.88</td>\n",
       "      <td>...</td>\n",
       "      <td>50.19</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.31</td>\n",
       "      <td>1.18e-01</td>\n",
       "      <td>1.18e-01</td>\n",
       "      <td>35.25</td>\n",
       "      <td>35.25</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>HN182</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>4.26</td>\n",
       "      <td>4.18</td>\n",
       "      <td>4.26</td>\n",
       "      <td>4.18</td>\n",
       "      <td>...</td>\n",
       "      <td>8.27</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.38e-01</td>\n",
       "      <td>9.38e-01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>77.30</td>\n",
       "      <td>16.60</td>\n",
       "      <td>77.30</td>\n",
       "      <td>16.60</td>\n",
       "      <td>...</td>\n",
       "      <td>81.07</td>\n",
       "      <td>3.77</td>\n",
       "      <td>3.77</td>\n",
       "      <td>4.88e-02</td>\n",
       "      <td>4.88e-02</td>\n",
       "      <td>60.70</td>\n",
       "      <td>60.70</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>HN182</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>4.26</td>\n",
       "      <td>13.73</td>\n",
       "      <td>4.26</td>\n",
       "      <td>13.73</td>\n",
       "      <td>...</td>\n",
       "      <td>17.41</td>\n",
       "      <td>3.68</td>\n",
       "      <td>3.68</td>\n",
       "      <td>2.68e-01</td>\n",
       "      <td>2.68e-01</td>\n",
       "      <td>9.47</td>\n",
       "      <td>9.47</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>HN182</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>4.18</td>\n",
       "      <td>13.73</td>\n",
       "      <td>4.18</td>\n",
       "      <td>13.73</td>\n",
       "      <td>...</td>\n",
       "      <td>17.34</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.61</td>\n",
       "      <td>2.63e-01</td>\n",
       "      <td>2.63e-01</td>\n",
       "      <td>9.55</td>\n",
       "      <td>9.55</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>10.28</td>\n",
       "      <td>77.30</td>\n",
       "      <td>10.28</td>\n",
       "      <td>77.30</td>\n",
       "      <td>...</td>\n",
       "      <td>79.63</td>\n",
       "      <td>2.33</td>\n",
       "      <td>2.33</td>\n",
       "      <td>3.02e-02</td>\n",
       "      <td>3.02e-02</td>\n",
       "      <td>67.02</td>\n",
       "      <td>67.02</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>HN182</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>4.18</td>\n",
       "      <td>46.14</td>\n",
       "      <td>4.18</td>\n",
       "      <td>46.14</td>\n",
       "      <td>...</td>\n",
       "      <td>48.39</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.25</td>\n",
       "      <td>4.88e-02</td>\n",
       "      <td>4.88e-02</td>\n",
       "      <td>41.96</td>\n",
       "      <td>41.96</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>HN160</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>7.50</td>\n",
       "      <td>91.50</td>\n",
       "      <td>7.50</td>\n",
       "      <td>91.50</td>\n",
       "      <td>...</td>\n",
       "      <td>92.13</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.64</td>\n",
       "      <td>6.97e-03</td>\n",
       "      <td>6.97e-03</td>\n",
       "      <td>83.99</td>\n",
       "      <td>83.99</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    patient  drug_id_A   drug_name_A  drug_id_B   drug_name_B  cluster_p  \\\n",
       "92    HN148        133   Doxorubicin       1012    Vorinostat          1   \n",
       "60    HN137        201  Epothilone B        302        PI-103          1   \n",
       "93    HN148        201  Epothilone B       1010     Gefitinib          1   \n",
       "46    HN137       1007     Docetaxel       1010     Gefitinib          1   \n",
       "164   HN160        133   Doxorubicin       1012    Vorinostat          1   \n",
       "56    HN137        133   Doxorubicin       1012    Vorinostat          1   \n",
       "24    HN120        201  Epothilone B        302        PI-103          1   \n",
       "96    HN148        201  Epothilone B        302        PI-103          1   \n",
       "128   HN159        133   Doxorubicin       1012    Vorinostat          1   \n",
       "10    HN120       1007     Docetaxel       1010     Gefitinib          1   \n",
       "118   HN159       1007     Docetaxel       1010     Gefitinib          1   \n",
       "20    HN120        133   Doxorubicin       1012    Vorinostat          1   \n",
       "154   HN160       1007     Docetaxel       1010     Gefitinib          1   \n",
       "153   HN160       1007     Docetaxel        201  Epothilone B          1   \n",
       "45    HN137       1007     Docetaxel        201  Epothilone B          1   \n",
       "81    HN148       1007     Docetaxel        201  Epothilone B          1   \n",
       "57    HN137        201  Epothilone B       1010     Gefitinib          1   \n",
       "165   HN160        201  Epothilone B       1010     Gefitinib          1   \n",
       "129   HN159        201  Epothilone B       1010     Gefitinib          1   \n",
       "82    HN148       1007     Docetaxel       1010     Gefitinib          1   \n",
       "200   HN182        133   Doxorubicin       1012    Vorinostat          1   \n",
       "132   HN159        201  Epothilone B        302        PI-103          1   \n",
       "117   HN159       1007     Docetaxel        201  Epothilone B          1   \n",
       "189   HN182       1007     Docetaxel        201  Epothilone B          1   \n",
       "21    HN120        201  Epothilone B       1010     Gefitinib          1   \n",
       "190   HN182       1007     Docetaxel       1010     Gefitinib          1   \n",
       "201   HN182        201  Epothilone B       1010     Gefitinib          1   \n",
       "9     HN120       1007     Docetaxel        201  Epothilone B          1   \n",
       "204   HN182        201  Epothilone B        302        PI-103          1   \n",
       "168   HN160        201  Epothilone B        302        PI-103          1   \n",
       "\n",
       "     cluster_kill_A  cluster_kill_B  kill_A  kill_B  ...  kill_C.1  improve  \\\n",
       "92            50.31           45.66   50.31   45.66  ...     73.00    22.69   \n",
       "60            70.44           74.00   70.44   74.00  ...     92.31    18.32   \n",
       "93            24.02           28.19   24.02   28.19  ...     45.44    17.25   \n",
       "46            22.47           21.40   22.47   21.40  ...     39.07    16.59   \n",
       "164           25.28           34.91   25.28   34.91  ...     51.37    16.46   \n",
       "56            74.37           61.77   74.37   61.77  ...     90.20    15.83   \n",
       "24            77.30           84.90   77.30   84.90  ...     96.57    11.67   \n",
       "96            24.02           59.74   24.02   59.74  ...     69.41     9.67   \n",
       "128           69.26           29.00   69.26   29.00  ...     78.17     8.91   \n",
       "10            10.28           16.60   10.28   16.60  ...     25.17     8.57   \n",
       "118            9.64           11.32    9.64   11.32  ...     19.87     8.55   \n",
       "20            86.28           60.44   86.28   60.44  ...     94.57     8.29   \n",
       "154            9.29           15.82    9.29   15.82  ...     23.64     7.82   \n",
       "153            9.29            7.50    9.29    7.50  ...     16.10     6.81   \n",
       "45            22.47           70.44   22.47   70.44  ...     77.08     6.64   \n",
       "81             8.65           24.02    8.65   24.02  ...     30.59     6.57   \n",
       "57            70.44           21.40   70.44   21.40  ...     76.76     6.33   \n",
       "165            7.50           15.82    7.50   15.82  ...     22.14     6.32   \n",
       "129           44.88           11.32   44.88   11.32  ...     51.12     6.24   \n",
       "82             8.65           28.19    8.65   28.19  ...     34.40     6.21   \n",
       "200            8.72           28.95    8.72   28.95  ...     35.14     6.20   \n",
       "132           44.88           86.86   44.88   86.86  ...     92.76     5.90   \n",
       "117            9.64           44.88    9.64   44.88  ...     50.19     5.31   \n",
       "189            4.26            4.18    4.26    4.18  ...      8.27     4.00   \n",
       "21            77.30           16.60   77.30   16.60  ...     81.07     3.77   \n",
       "190            4.26           13.73    4.26   13.73  ...     17.41     3.68   \n",
       "201            4.18           13.73    4.18   13.73  ...     17.34     3.61   \n",
       "9             10.28           77.30   10.28   77.30  ...     79.63     2.33   \n",
       "204            4.18           46.14    4.18   46.14  ...     48.39     2.25   \n",
       "168            7.50           91.50    7.50   91.50  ...     92.13     0.64   \n",
       "\n",
       "     improve.1  improve_p  improve_p.1  sum_kill_dif  sum_kill_dif.1  \\\n",
       "92       22.69   4.51e-01     4.51e-01          4.65            4.65   \n",
       "60       18.32   2.48e-01     2.48e-01          3.56            3.56   \n",
       "93       17.25   6.12e-01     6.12e-01          4.16            4.16   \n",
       "46       16.59   7.38e-01     7.38e-01          1.07            1.07   \n",
       "164      16.46   4.71e-01     4.71e-01          9.63            9.63   \n",
       "56       15.83   2.13e-01     2.13e-01         12.60           12.60   \n",
       "24       11.67   1.37e-01     1.37e-01          7.60            7.60   \n",
       "96        9.67   1.62e-01     1.62e-01         35.72           35.72   \n",
       "128       8.91   1.29e-01     1.29e-01         40.26           40.26   \n",
       "10        8.57   5.16e-01     5.16e-01          6.33            6.33   \n",
       "118       8.55   7.55e-01     7.55e-01          1.68            1.68   \n",
       "20        8.29   9.61e-02     9.61e-02         25.84           25.84   \n",
       "154       7.82   4.94e-01     4.94e-01          6.53            6.53   \n",
       "153       6.81   7.33e-01     7.33e-01          1.79            1.79   \n",
       "45        6.64   9.43e-02     9.43e-02         47.96           47.96   \n",
       "81        6.57   2.74e-01     2.74e-01         15.37           15.37   \n",
       "57        6.33   8.98e-02     8.98e-02         49.03           49.03   \n",
       "165       6.32   3.99e-01     3.99e-01          8.32            8.32   \n",
       "129       6.24   1.39e-01     1.39e-01         33.56           33.56   \n",
       "82        6.21   2.20e-01     2.20e-01         19.54           19.54   \n",
       "200       6.20   2.14e-01     2.14e-01         20.22           20.22   \n",
       "132       5.90   6.79e-02     6.79e-02         41.98           41.98   \n",
       "117       5.31   1.18e-01     1.18e-01         35.25           35.25   \n",
       "189       4.00   9.38e-01     9.38e-01          0.08            0.08   \n",
       "21        3.77   4.88e-02     4.88e-02         60.70           60.70   \n",
       "190       3.68   2.68e-01     2.68e-01          9.47            9.47   \n",
       "201       3.61   2.63e-01     2.63e-01          9.55            9.55   \n",
       "9         2.33   3.02e-02     3.02e-02         67.02           67.02   \n",
       "204       2.25   4.88e-02     4.88e-02         41.96           41.96   \n",
       "168       0.64   6.97e-03     6.97e-03         83.99           83.99   \n",
       "\n",
       "               Combi Name 1            Combi Name 2              Combi Name  \n",
       "92   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "60      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "93   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "46      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "164  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "56   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "24      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "96      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "128  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "10      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "118     Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "20   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "154     Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "153  Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "45   Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "81   Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "57   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "165  Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "129  Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "82      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "200  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "132     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "117  Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "189  Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "21   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "190     Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "201  Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "9    Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "204     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "168     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "\n",
       "[30 rows x 21 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_df.sort_values('improve', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare for all lines and drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:01.188733Z",
     "start_time": "2020-08-24T05:17:01.174952Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_kill_df = pred_single_df[['patient', 'drug_name', 'kill']].pivot(index='drug_name', columns='patient', values='kill')\n",
    "#pred_single_delta_df = pred_single_df[['patient', 'drug_name', 'delta']].pivot(index='drug_name', columns='patient', values='delta')\n",
    "pred_combi_kill_df = pred_combi_df[['patient', 'Combi Name', 'kill_C']].pivot(index='Combi Name', columns='patient', values='kill_C')\n",
    "\n",
    "obs_single_kill_df = obs_kill_df.loc[single_drug_list, patient_list]\n",
    "obs_combi_kill_df = obs_kill_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:01.306988Z",
     "start_time": "2020-08-24T05:17:01.300854Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.25)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "cmap = plt.cm.get_cmap('tab10', 10)\n",
    "colors = cmap(np.linspace(0, 1, 10))\n",
    "patient_color_dict = dict(zip(patient_list, colors[0:len(patient_list)]))\n",
    "drug_color_dict = dict(zip(single_drug_list, colors[0:len(single_drug_list)]))\n",
    "combi_color_dict = dict(zip(combi_drug_list, colors[0:len(combi_drug_list)]))\n",
    "\n",
    "drug_marker_dict = dict(zip(single_drug_list, ['o', 'v', '^', '<', '>', 'd', 's']))\n",
    "combi_marker_dict = dict(zip(combi_drug_list, ['p', '*', 'P', 'X', 'h']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For single drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:01.580158Z",
     "start_time": "2020-08-24T05:17:01.551152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>72.39</td>\n",
       "      <td>84.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>77.52</td>\n",
       "      <td>74.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>81.74</td>\n",
       "      <td>59.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>84.66</td>\n",
       "      <td>86.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>51.99</td>\n",
       "      <td>91.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug                              File name Replicate Patient  \\\n",
       "120  PI-103  validation_replicates_2019_06_24.xlsx         2   HN120   \n",
       "121  PI-103  validation_replicates_2019_06_24.xlsx         2   HN137   \n",
       "122  PI-103  validation_replicates_2019_06_24.xlsx         2   HN148   \n",
       "123  PI-103  validation_replicates_2019_06_24.xlsx         2   HN159   \n",
       "124  PI-103  validation_replicates_2019_06_24.xlsx         2   HN160   \n",
       "\n",
       "     Observed % cell death  Predicted % cell death  \n",
       "120                  72.39                   84.90  \n",
       "121                  77.52                   74.00  \n",
       "122                  81.74                   59.74  \n",
       "123                  84.66                   86.86  \n",
       "124                  51.99                   91.50  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "\n",
    "pred_df = pred_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "pred_df = pred_single_kill_df.loc[[d[0] for d in obs_single_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug', 'Patient', 'Predicted % cell death']\n",
    "\n",
    "scatter_single_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_single_df.loc[:, 'Replicate'] = scatter_single_df['Replicate'].astype(str)\n",
    "print (scatter_single_df.shape)\n",
    "scatter_single_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:01.681397Z",
     "start_time": "2020-08-24T05:17:01.679281Z"
    }
   },
   "outputs": [],
   "source": [
    "# scatter_single_df = scatter_single_df[scatter_single_df['Patient'].isin(['HN137'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T05:17:02.310161Z",
     "start_time": "2020-08-24T05:17:01.809916Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single drug | Pearson r = 0.62 (1.93e-05)\n",
      "Single drug [R-sq 2.64%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Patient', style='Drug', s=150, alpha=0.9, ax=ax)\n",
    "\n",
    "for _, row in scatter_single_df.groupby(['Drug', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "    \n",
    "\n",
    "vmin = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Single drug | Pearson r = {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Single drug [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "elif dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "fig.savefig('../figure/Fig4E_single_drug_{}_bulk.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For combi drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:24.734821Z",
     "start_time": "2020-07-12T04:55:24.699064Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 5)\n",
      "(70, 3)\n",
      "(70, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>82.03</td>\n",
       "      <td>96.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>90.08</td>\n",
       "      <td>92.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>85.65</td>\n",
       "      <td>69.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>88.91</td>\n",
       "      <td>92.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>55.05</td>\n",
       "      <td>92.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug combination                              File name Replicate  \\\n",
       "65  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "66  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "67  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "68  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "69  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "\n",
       "   Patient  Observed % cell death  Predicted % cell death  \n",
       "65   HN120                  82.03                   96.57  \n",
       "66   HN137                  90.08                   92.31  \n",
       "67   HN148                  85.65                   69.41  \n",
       "68   HN159                  88.91                   92.76  \n",
       "69   HN160                  55.05                   92.13  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_combi_kill_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "print (obs_df.shape)\n",
    "\n",
    "pred_df = pred_combi_kill_df.loc[[d[0] for d in obs_combi_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % cell death']\n",
    "print (pred_df.shape)\n",
    "\n",
    "scatter_combi_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_combi_df.loc[:, 'Replicate'] = scatter_combi_df['Replicate'].astype(str)\n",
    "print (scatter_combi_df.shape)\n",
    "scatter_combi_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.323401Z",
     "start_time": "2020-07-12T04:55:24.737869Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drug combination | 0.62 (1.04e-03)\n",
      "Drug combination [R-sq -13.97%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Drug combination', style='Patient', s=150, alpha=1.0)\n",
    "\n",
    "for _, row in scatter_combi_df.groupby(['Drug combination', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "\n",
    "vmin = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Drug combination | {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Drug combination [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "if dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "fig.savefig('../figure/Fig4_combi_drug_{}_bulk.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare improvement of drug combi over single drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.347083Z",
     "start_time": "2020-07-12T04:55:25.325479Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_combi_imp_df = pred_combi_df[['patient', 'Combi Name', 'improve']].pivot(index='Combi Name', columns='patient', values='improve')\n",
    "pred_combi_pimp_df = pred_combi_df[['patient', 'Combi Name', 'improve_p']].pivot(index='Combi Name', columns='patient', values='improve_p')\n",
    "\n",
    "pred_combi_imp_df = pred_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "pred_combi_pimp_df = pred_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.362685Z",
     "start_time": "2020-07-12T04:55:25.349260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>patient</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Combi Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>2.33</td>\n",
       "      <td>6.64</td>\n",
       "      <td>6.57</td>\n",
       "      <td>5.31</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>8.57</td>\n",
       "      <td>16.59</td>\n",
       "      <td>6.21</td>\n",
       "      <td>8.55</td>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>3.77</td>\n",
       "      <td>6.33</td>\n",
       "      <td>17.25</td>\n",
       "      <td>6.24</td>\n",
       "      <td>6.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>11.67</td>\n",
       "      <td>18.32</td>\n",
       "      <td>9.67</td>\n",
       "      <td>5.90</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>8.29</td>\n",
       "      <td>15.83</td>\n",
       "      <td>22.69</td>\n",
       "      <td>8.91</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "patient                 HN120  HN137  HN148  HN159  HN160\n",
       "Combi Name                                               \n",
       "Docetaxel|Epothilone B   2.33   6.64   6.57   5.31   6.81\n",
       "Docetaxel|Gefitinib      8.57  16.59   6.21   8.55   7.82\n",
       "Gefitinib|Epothilone B   3.77   6.33  17.25   6.24   6.32\n",
       "Epothilone B|PI-103     11.67  18.32   9.67   5.90   0.64\n",
       "Doxorubicin|Vorinostat   8.29  15.83  22.69   8.91  16.46"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_imp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.386509Z",
     "start_time": "2020-07-12T04:55:25.365208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>55.10</td>\n",
       "      <td>64.60</td>\n",
       "      <td>39.32</td>\n",
       "      <td>44.73</td>\n",
       "      <td>25.05</td>\n",
       "      <td>60.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>43.22</td>\n",
       "      <td>46.48</td>\n",
       "      <td>57.34</td>\n",
       "      <td>42.40</td>\n",
       "      <td>8.10</td>\n",
       "      <td>37.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>46.41</td>\n",
       "      <td>55.18</td>\n",
       "      <td>41.84</td>\n",
       "      <td>23.98</td>\n",
       "      <td>14.82</td>\n",
       "      <td>56.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>82.10</td>\n",
       "      <td>90.42</td>\n",
       "      <td>86.49</td>\n",
       "      <td>89.11</td>\n",
       "      <td>56.33</td>\n",
       "      <td>40.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>67.57</td>\n",
       "      <td>70.24</td>\n",
       "      <td>63.18</td>\n",
       "      <td>61.33</td>\n",
       "      <td>23.57</td>\n",
       "      <td>66.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        HN120  HN137  HN148  HN159  HN160  HN182\n",
       "Drug                                                            \n",
       "Docetaxel|Epothilone B  55.10  64.60  39.32  44.73  25.05  60.92\n",
       "Docetaxel|Gefitinib     43.22  46.48  57.34  42.40   8.10  37.92\n",
       "Doxorubicin|Vorinostat  46.41  55.18  41.84  23.98  14.82  56.37\n",
       "Epothilone B|PI-103     82.10  90.42  86.49  89.11  56.33  40.31\n",
       "Gefitinib|Epothilone B  67.57  70.24  63.18  61.33  23.57  66.17"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEMPORARY: using averge response\n",
    "\n",
    "temp_df = obs_kill_df.loc[combi_drug_list].reset_index().drop(['File name', 'Replicate'], axis=1)\n",
    "temp_df = temp_df.groupby('Drug').mean()\n",
    "# temp_df = temp_df.loc[~temp_df.index.duplicated(keep='first')]\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.419157Z",
     "start_time": "2020-07-12T04:55:25.389253Z"
    }
   },
   "outputs": [],
   "source": [
    "imp_results = []\n",
    "pimp_results = []\n",
    "for c, data in temp_df.loc[combi_drug_list, patient_list].iterrows():\n",
    "    a, b = c.split('|')\n",
    "    \n",
    "    c_kill = data.values\n",
    "    best_kill = obs_kill_df.loc[[a, b]].max()[patient_list]\n",
    "#     best_kill = obs_kill_df.loc[[a, b]].reset_index().groupby('Drug').mean()[patient_list].max()\n",
    "    \n",
    "#     print (c, c_kill, best_kill)\n",
    "    \n",
    "    imp = list((c_kill - best_kill).values)\n",
    "    pimp = list(((c_kill - best_kill)/best_kill).values)\n",
    "    \n",
    "    imp_results += [[c] + imp]\n",
    "    pimp_results += [[c] + pimp]\n",
    "        \n",
    "obs_combi_imp_df = pd.DataFrame(imp_results)\n",
    "obs_combi_imp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_imp_df = obs_combi_imp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_pimp_df = pd.DataFrame(pimp_results)\n",
    "obs_combi_pimp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_imp_df = obs_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.901242Z",
     "start_time": "2020-07-12T04:55:25.420971Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_df = obs_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'Patient', 'Observed % death increase']\n",
    "\n",
    "pred_df = pred_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % death increase']\n",
    "\n",
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "scatter_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "\n",
    "sns.scatterplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', hue='Drug combination', style='Patient', s=100, alpha=0.7)\n",
    "# sns.regplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', x_ci='ci', ci=99, scatter=False, color='grey')\n",
    "# plt.plot([0, 25], [0, 25], ls=\"--\", c=\".3\")\n",
    "\n",
    "vmin = scatter_df[['Observed % death increase', 'Predicted % death increase']].min().min()\n",
    "vmax = scatter_df[['Observed % death increase', 'Predicted % death increase']].max().max()\n",
    "\n",
    "# ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "# ax.set_xlim((vmin-5, vmax+5))\n",
    "# ax.set_ylim((vmin-5, vmax+5))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0)\n",
    "\n",
    "scor, pval = stats.pearsonr(obs_df['Observed % death increase'].values, pred_df['Predicted % death increase'].values)\n",
    "ax.set_title('Single VS Combi\\n(Pearson r = {:.2f} p-val < {:.2e})'.format(scor, pval))\n",
    "\n",
    "# r2 = metrics.r2_score(scatter_df['Observed % death increase'].values, scatter_df['Predicted % death increase'].values)\n",
    "# ax.set_title('Single VS Combi [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "plt.tight_layout()\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_scatter_bulk.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.921021Z",
     "start_time": "2020-07-12T04:55:25.903592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % death increase</th>\n",
       "      <th>Predicted % death increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN148</td>\n",
       "      <td>10.81</td>\n",
       "      <td>22.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN137</td>\n",
       "      <td>12.90</td>\n",
       "      <td>18.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>10.23</td>\n",
       "      <td>17.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN137</td>\n",
       "      <td>7.87</td>\n",
       "      <td>16.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN137</td>\n",
       "      <td>2.80</td>\n",
       "      <td>15.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN120</td>\n",
       "      <td>6.09</td>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN148</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN159</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.35</td>\n",
       "      <td>8.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN159</td>\n",
       "      <td>6.73</td>\n",
       "      <td>8.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN120</td>\n",
       "      <td>7.37</td>\n",
       "      <td>8.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-6.17</td>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-3.92</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>6.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>-12.89</td>\n",
       "      <td>6.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>5.43</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-5.40</td>\n",
       "      <td>6.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>13.98</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN148</td>\n",
       "      <td>4.39</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN159</td>\n",
       "      <td>3.80</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>5.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.19</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN160</td>\n",
       "      <td>4.34</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Drug combination Patient  Observed % death increase  \\\n",
       "22  Doxorubicin|Vorinostat   HN148                      10.81   \n",
       "16     Epothilone B|PI-103   HN137                      12.90   \n",
       "12  Gefitinib|Epothilone B   HN148                      10.23   \n",
       "6      Docetaxel|Gefitinib   HN137                       7.87   \n",
       "24  Doxorubicin|Vorinostat   HN160                      -3.07   \n",
       "21  Doxorubicin|Vorinostat   HN137                       2.80   \n",
       "15     Epothilone B|PI-103   HN120                       6.09   \n",
       "17     Epothilone B|PI-103   HN148                       4.75   \n",
       "23  Doxorubicin|Vorinostat   HN159                      -1.82   \n",
       "5      Docetaxel|Gefitinib   HN120                       9.35   \n",
       "8      Docetaxel|Gefitinib   HN159                       6.73   \n",
       "20  Doxorubicin|Vorinostat   HN120                       7.37   \n",
       "9      Docetaxel|Gefitinib   HN160                      -6.17   \n",
       "4   Docetaxel|Epothilone B   HN160                      -3.92   \n",
       "1   Docetaxel|Epothilone B   HN137                      -0.21   \n",
       "2   Docetaxel|Epothilone B   HN148                     -12.89   \n",
       "11  Gefitinib|Epothilone B   HN137                       5.43   \n",
       "14  Gefitinib|Epothilone B   HN160                      -5.40   \n",
       "13  Gefitinib|Epothilone B   HN159                      13.98   \n",
       "7      Docetaxel|Gefitinib   HN148                       4.39   \n",
       "18     Epothilone B|PI-103   HN159                       3.80   \n",
       "3   Docetaxel|Epothilone B   HN159                      -2.62   \n",
       "10  Gefitinib|Epothilone B   HN120                       9.19   \n",
       "0   Docetaxel|Epothilone B   HN120                      -3.28   \n",
       "19     Epothilone B|PI-103   HN160                       4.34   \n",
       "\n",
       "    Predicted % death increase  \n",
       "22                       22.69  \n",
       "16                       18.32  \n",
       "12                       17.25  \n",
       "6                        16.59  \n",
       "24                       16.46  \n",
       "21                       15.83  \n",
       "15                       11.67  \n",
       "17                        9.67  \n",
       "23                        8.91  \n",
       "5                         8.57  \n",
       "8                         8.55  \n",
       "20                        8.29  \n",
       "9                         7.82  \n",
       "4                         6.81  \n",
       "1                         6.64  \n",
       "2                         6.57  \n",
       "11                        6.33  \n",
       "14                        6.32  \n",
       "13                        6.24  \n",
       "7                         6.21  \n",
       "18                        5.90  \n",
       "3                         5.31  \n",
       "10                        3.77  \n",
       "0                         2.33  \n",
       "19                        0.64  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_df.sort_values('Predicted % death increase', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.933122Z",
     "start_time": "2020-07-12T04:55:25.925158Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def change_boxplot_edge_color(ax, col):\n",
    "    for i, artist in enumerate(ax.artists):\n",
    "        # Set the linecolor on the artist to the facecolor, and set the facecolor to None\n",
    "        artist.set_edgecolor(col)\n",
    "        # artist.set_facecolor('None')\n",
    "\n",
    "        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "        # Loop over them here, and use the same colour as above\n",
    "        for j in range(i*6,i*6+6):\n",
    "            line = ax.lines[j]\n",
    "            line.set_color(col)\n",
    "            line.set_mfc(col)\n",
    "            line.set_mec(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:25.941495Z",
     "start_time": "2020-07-12T04:55:25.936852Z"
    }
   },
   "outputs": [],
   "source": [
    "cutoff = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:26.393244Z",
     "start_time": "2020-07-12T04:55:25.945473Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RanksumsResult(statistic=1.8613262649346203, pvalue=0.06269811626098261) Ttest_indResult(statistic=1.982555483580293, pvalue=0.059486223525144925)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(3, 4))\n",
    "\n",
    "merge_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "merge_df.loc[:, 'Observed increase'] = merge_df['Observed % death increase'] >= cutoff\n",
    "\n",
    "sns.boxplot(data=merge_df, x='Observed increase', y='Predicted % death increase', fliersize=4, color='lightblue', ax=ax)\n",
    "sns.swarmplot(data=merge_df, x='Observed increase', y='Predicted % death increase', color='black', alpha=0.5, ax=ax)\n",
    "change_boxplot_edge_color(ax, 'black')\n",
    "ax.set_xlabel('Observed increase ' + r'$\\geq {}\\%$'.format(cutoff))\n",
    "\n",
    "x = merge_df.loc[merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "y = merge_df.loc[~merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "\n",
    "sns.despine()\n",
    "print (stats.ranksums(x, y), stats.ttest_ind(x, y))\n",
    "plt.tight_layout()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_{}_bulk.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:26.401747Z",
     "start_time": "2020-07-12T04:55:26.395397Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.86 (6.27e-02)\n"
     ]
    }
   ],
   "source": [
    "print (\"{:.2f} ({:.2e})\".format(*stats.ranksums(x, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:26.413158Z",
     "start_time": "2020-07-12T04:55:26.404437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 14, 1: 11})"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = (merge_df['Observed % death increase'].values >= cutoff).astype(int)\n",
    "pred = (merge_df['Predicted % death increase'].values >= cutoff).astype(int)\n",
    "pred_val = merge_df['Predicted % death increase'].values\n",
    "\n",
    "Counter(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:26.426833Z",
     "start_time": "2020-07-12T04:55:26.415750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.48, 0.6060606060606061, 0.23529411764705882)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(obs, pred), metrics.f1_score(obs, pred, pos_label=1), metrics.f1_score(obs, pred, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-12T04:55:26.713350Z",
     "start_time": "2020-07-12T04:55:26.429278Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7302016251905508\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.1)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = metrics.precision_recall_curve(obs, pred_val)\n",
    "plt.plot(recall, precision)\n",
    "\n",
    "print (metrics.auc(recall, precision))\n",
    "\n",
    "no_skill = len(obs[obs==1]) / len(obs)\n",
    "plt.axhline(y=no_skill)\n",
    "\n",
    "plt.xlim((0,1))\n",
    "plt.ylim((0,1.1))\n",
    "\n",
    "# fig.savefig('../figure/Fig4_pr_curve_{}_{}.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
